4 ms·

The authors initial feelings around ML are similar to how I feel. It's such a broad subject, it feels like you could read/study for years and not cover everythi

by vertis 10y ago

The authors initial feelings around ML are similar to how I feel. It's such a broad subject, it feels like you could read/study for years and not cover everything. Worse still it's ever changing.

amelius 10y ago
I'd like to see a high-level overview of the kind of problems that can be solved by the different kinds of ML, and their applications.
- denzil_correa 10y ago
  The only material I have seen which does this or comes to this is the Andrew Ng Machine Learning class on Coursera and Academic Earth.
- jimbokun 10y ago
  Try Pedro Domingos "The Master Algorithm". Good high level overview of the various "schools" of Machine Learning. Not sure if it identifies which problems are best solved by which approach, though. More the history of how they have taken turns as the most successful paradigm.
chestervonwinch 10y ago
I don't know about that. It might appear that way, if you only read news articles on ML since these usually are announcements of the state-of-the-art. However, compare the topics covered in Duda and Hart's text (1st edition, 1973; 2nd edition 1995) to the more recent text by Hastie et al. (current edition ~2013). There isn't a huge difference in subject matter. The latter is slightly more advanced and has more of a statistics perspective, but the foundations are there: Bayes' decision theory, linear methods for classification / regression, naive bayes, neural networks, decision trees, ensembles, clustering, etc... There is a range of topics that are foundational to ML, and thus, relatively stable. These topics are built upon the even more solid foundations of probability theory and statistics. The biggest advances in ML in the last decade (I would argue) were not due to advances in theory.
- Florin_Andrei 10y ago
  > the more recent text by Hastie et al. Is that 'The elements of statistical learning'?
  - chestervonwinch 10y ago
    That's what I was referring to, yes.
apathy 10y ago
You just described all fields worth doing research in. If we knew the right answers we wouldn't waste our time implementing the wrong ones. It's not a bad thing: it means there is room for you to discover something genuinely new and understand something, however small, that nobody else ever completely understood. I think that's great. But YMMV.

by vertis 10y ago

The authors initial feelings around ML are similar to how I feel. It's such a broad subject, it feels like you could read/study for years and not cover everything. Worse still it's ever changing.

amelius 10y ago

I'd like to see a high-level overview of the kind of problems that can be solved by the different kinds of ML, and their applications.

denzil_correa 10y ago
The only material I have seen which does this or comes to this is the Andrew Ng Machine Learning class on Coursera and Academic Earth.
jimbokun 10y ago
Try Pedro Domingos "The Master Algorithm". Good high level overview of the various "schools" of Machine Learning. Not sure if it identifies which problems are best solved by which approach, though. More the history of how they have taken turns as the most successful paradigm.

chestervonwinch 10y ago

I don't know about that. It might appear that way, if you only read news articles on ML since these usually are announcements of the state-of-the-art. However, compare the topics covered in Duda and Hart's text (1st edition, 1973; 2nd edition 1995) to the more recent text by Hastie et al. (current edition ~2013). There isn't a huge difference in subject matter. The latter is slightly more advanced and has more of a statistics perspective, but the foundations are there: Bayes' decision theory, linear methods for classification / regression, naive bayes, neural networks, decision trees, ensembles, clustering, etc... There is a range of topics that are foundational to ML, and thus, relatively stable. These topics are built upon the even more solid foundations of probability theory and statistics. The biggest advances in ML in the last decade (I would argue) were not due to advances in theory.

Florin_Andrei 10y ago
> the more recent text by Hastie et al. Is that 'The elements of statistical learning'?
- chestervonwinch 10y ago
  That's what I was referring to, yes.

apathy 10y ago

You just described all fields worth doing research in. If we knew the right answers we wouldn't waste our time implementing the wrong ones. It's not a bad thing: it means there is room for you to discover something genuinely new and understand something, however small, that nobody else ever completely understood. I think that's great. But YMMV.